本次澳洲代写主要为Python数据分析的assignment

Data Set Description and Preparation

Your task in this assignment is to calculate a bushﬁre risk score with regard to bushﬁre protection

for different neighbourhoods in Sydney. The neighbourhood ’score’ is expressed as a measure

of several factors which we assume to affect the risk of bush ﬁres within an area — vegetation,

population density, number of dwellings etc.

In order to calculate this score, you will need to integrate different data sources. As a starting

point, we provide you with some census-based datasets which give you input on at least three

factors: population density, dwelling and business locations. We also provide some spatial data

with the vegetation and risk categories provided by the NSW Rural Fire Service. We leave it up-

to you to integrate further data and to reﬁne the suggested risk score. Some ideas would be the

availability of speciﬁc emergency services, or the prevalence of waterways etc.

Based on your computed risk scores, perform then a correlation analysis against the ABS pro-

vided median income and median rent costs of each neighbourhood.

Your submission should consist of your Jupyter notebook that you used for integrating the data

sets and for performing and visualising your analysis.

Milestone 1: Load and integrate the provided datasets into the university provided PostgreSQL

database by the tutorials in Week 11.

Provided datasets: We provide in Canvas several CSV ﬁles with Statistical Area 2 (SA2) data

from the Australian Bureau of Statistics (ABS), as well as some bush ﬁre prone land vegetation

spatial data from the NSW Rural Fire Service (keep checking Canvas/Ed for any later additions or

updates at https://edstem.org/courses/5592/discussion/462995):

StatisticalAreas.csv: area id, area name, parent area id

Neighbourhoods.csv: area id, area name, land area, population, dwellings, businesses, median income, av

BusinessStats.csv: area id, number of businesses, accommodation and food, retail trade, agriculture for

RFSNSW BFPL.shp: gid, category, shape leng, shape area, geom

Task 1: Data Integration and Database Generation

Build a database using PostgreSQL that integrates data from the following sources:

1. Sydney neighbourhood dataset (based on provided CSV ﬁles with SA2-data from ABS).

2. Spatial data in the SA2 ESRI Shape data ﬁle from the ABS at https://www.abs.gov.au/

AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.001July%202016)

3. Census data for the given neighbourhoods including population count, dwelling and busi-

nesses counts.

4. Bush Fire Prone Land in NSW; Originally from the Rural Fire service, but modiﬁed for this

task – you will need to do some transforming of this data

5. You are encouraged to extend and reﬁne both scoring function and source data. For

full points when integrating at least one additional data set.

Milestone 1: Load and integrate the provided datasets into PostgreSQL by the tutorials in Week 11.

Task 2: Fire Risk Analysis

1. Compute the ﬁre risk score for all given neighbourhoods according to the following formula

and deﬁnitions (adjust as needed if you integrated any additional datasets):

re risk = S(z(population density)+z(dwelling & business density)+z(bfpl density) z(assistive service density)

With S being the logistic function (sigmoid function), and z the z-score (”standard score”) of a

measure – the number of standard deviations from the mean (assuming a normal distribution):

z(measure; x) = x avgmeasure

stddevmeasure

Measure Deﬁnition Risk Data Source

population density population divided by neighbourhood’s land area + Neighbourhoods.csv

dwelling density number of dwellings divided by neighbourhood land area + Neighbourhoods.csv

business density number of businesses divided by neighbourhood land area + BusinessStats.csv

bfpl density area and category of BFPL divided by neighbourhood land area + RFSNSW BFPL.shp

assistive service density number of assistive services divided by neighbourhood land area – BusinessStats.csv

2. Store the computed measures and scores of each neighbourhood in your database. Create

at least one index which is helpful for data integration or the ﬁre risk score computation.

3. Determine whether there is a correlation between your ﬁre risk score and the median income

and rent of a neighbourhood.

Task 3: Documentation of your Bushre Risk Analysis

Write a document (Jupyter notebook or Word document or PDF ﬁle, no more than 5 pages plus

optional Appendix) in which you document your data integration steps and the main outcomes

of your ﬁre risk data analysis, including the correlation study with the bush ﬁre statistics. Your

document should contain the following:

1. Dataset Description

What are your data sources and how did you obtain and pre-process the data?

2. Database Description

Into which database schema did you integrate your data (preferable shown with a diagram)?

Which index(es) did you create, and why?

3. Fire Risk Score Analysis

Show which formula you applied to compute the Fire Risk score per neighbourhood, and

give an overview of ﬁre risk results. This can be done either in text by highlighting some

representative results, or with a graphical representation onto a map (preferred).

4. Correlation Analysis

How well does your score correlate to the afﬂuence of the neighbourhoods? Compare both

the median household incomes and the rental prices of each region.

**程序代写代做C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB**

本网站支持淘宝 支付宝 微信支付 paypal等等交易。如果不放心可以用淘宝交易！

**E-mail:** itcsdx@outlook.com **微信:**itcsdx

如果您使用手机请先保存二维码，微信识别。如果用电脑，直接掏出手机果断扫描。